Diabetic retinopathy is an eye disease which is associated with long-standing diabetes. Vision impairment may be prevented using laser treatments if diabetic retinopathy is detected early. However, early detection of diabetic retinopathy is challenging as diabetic retinopathy does not show explicit symptoms until it reaches advance stages.
Conventionally, diabetic retinopathy is detected manually by physicians and ophthalmologists. However, manual detection of diabetic retinopathy has many disadvantages such as lack of experience. Also, manually detecting diabetic retinopathy is a time-consuming process. Further, delay in screening process leads to delayed or no follow-up, miscommunication and delayed treatment thereby increasing the probability of vision loss.
To overcome the disadvantages of manual detection, systems and methods exist that facilitate automatic detection of retinopathy. For instance, systems exist that use training data to build datasets and algorithms for detecting retinopathy from digital fundus images. However, the above-mentioned systems also suffer from various disadvantages. The abovementioned systems are incapable of processing noisy images, out of focus images, underexposed and overexposed images. Also, these systems are not able to predict retinopathy with certainty thereby facilitating need of a confirmatory screening by specialists.
In light of the abovementioned disadvantages, there is a need for a system and method for automated detection of retinopathy, particularly diabetic retinopathy. Further, there is a need for a system and method which is capable of efficiently processing images captured from color fundus camera for detecting retinopathy. Furthermore, there is a need for a system and method capable of accurately detecting retinopathy and if required, promptly referring the patients to specialists. In addition, there is a need for a learning based system and method that uses pattern recognition with feedback loop. Also, there is a need for a system and method that is scalable, cost-effective, capable of processing multiple images, lowers dependency on human intervention and facilitates in providing more time to medical practitioners.